refactor: remove unused imports in component panel

- 在插件安装或启用后，自动检测并提示指令冲突。
- 当检测到指令冲突时，显示警告对话框，告知用户冲突数量及可能的影响。

.github/workflows/build-app.yml

@@ -1,79 +0,0 @@
-name: Build Desktop App
-
-on:
-  push:
-    tags:
-      - 'v*'
-  workflow_dispatch:
-
-jobs:
-  build:
-    strategy:
-      fail-fast: false
-      matrix:
-        platform: [macos-latest, ubuntu-latest, windows-latest]
-
-    runs-on: ${{ matrix.platform }}
-    
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.10'
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: 20
-
-      - name: Install Rust
-        uses: dtolnay/rust-toolchain@stable
-
-      - name: Install dependencies (Ubuntu)
-        if: matrix.platform == 'ubuntu-latest'
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y libgtk-3-dev libwebkit2gtk-4.0-dev libappindicator3-dev librsvg2-dev patchelf
-
-      - name: Install Python dependencies
-        run: |
-          pip install uv
-          uv sync
-
-      - name: Build Python backend with Nuitka
-        run: |
-          pip install nuitka
-          python build_nuitka.py
-
-      - name: Install Node dependencies
-        working-directory: ./dashboard
-        run: npm install
-
-      - name: Build Tauri app
-        working-directory: ./dashboard
-        run: npm run tauri:build
-
-      - name: Upload artifacts (macOS)
-        if: matrix.platform == 'macos-latest'
-        uses: actions/upload-artifact@v4
-        with:
-          name: astrbot-macos
-          path: dashboard/src-tauri/target/release/bundle/dmg/*.dmg
-
-      - name: Upload artifacts (Windows)
-        if: matrix.platform == 'windows-latest'
-        uses: actions/upload-artifact@v4
-        with:
-          name: astrbot-windows
-          path: dashboard/src-tauri/target/release/bundle/msi/*.msi
-
-      - name: Upload artifacts (Linux)
-        if: matrix.platform == 'ubuntu-latest'
-        uses: actions/upload-artifact@v4
-        with:
-          name: astrbot-linux
-          path: |
-            dashboard/src-tauri/target/release/bundle/deb/*.deb
-            dashboard/src-tauri/target/release/bundle/appimage/*.AppImage

.github/workflows/dashboard_ci.yml

@@ -36,7 +36,7 @@ jobs:
           zip -r dist.zip dist
 
       - name: Archive production artifacts
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v5
         with:
           name: dist-without-markdown
           path: |

.gitignore

@@ -32,7 +32,6 @@ tests/astrbot_plugin_openai
 # Dashboard
 dashboard/node_modules/
 dashboard/dist/
-dashboard/src-tauri/target
 package-lock.json
 package.json
 yarn.lock
@@ -49,6 +48,5 @@ astrbot.lock
 chroma
 venv/*
 pytest.ini
-build/
 AGENTS.md
 IFLOW.md

BUILD_INSTRUCTIONS.md

@@ -1,287 +0,0 @@
-# AstrBot 桌面应用构建指南
-
-本指南介绍如何使用 Nuitka 将 Python 后端打包并集成到 Tauri 桌面应用中。
-
-## 前置要求
-
-### 系统要求
-- Python 3.10+
-- Node.js 20+
-- Rust (通过 rustup 安装)
-- UV 包管理器
-
-### macOS 额外要求
-- Xcode Command Line Tools: `xcode-select --install`
-
-### Linux 额外要求
-```bash
-sudo apt-get install -y libgtk-3-dev libwebkit2gtk-4.0-dev \
-  libappindicator3-dev librsvg2-dev patchelf
-```
-
-### Windows 额外要求
-- Visual Studio 2019+ with C++ build tools
-- Windows 10 SDK
-
-## 构建步骤
-
-### 1. 安装 Python 依赖
-```bash
-pip install uv
-uv sync
-```
-
-### 2. 安装 Nuitka
-```bash
-pip install nuitka
-```
-
-### 3. 构建 Python 后端
-```bash
-python build_nuitka.py
-```
-
-这会使用 Nuitka 将 `main.py` 编译为独立可执行文件，输出到 `build/nuitka/` 目录。
-
-**注意**: Nuitka 编译过程可能需要 10-30 分钟，取决于您的系统性能。
-
-### 4. 安装前端依赖
-```bash
-cd dashboard
-npm install
-```
-
-### 5. 构建 Tauri 应用
-```bash
-npm run tauri:build
-```
-
-构建脚本会自动:
-1. 运行 `build_nuitka.py` 编译 Python 后端
-2. 将编译好的可执行文件复制到 `src-tauri/resources/` 目录
-3. 构建 Tauri 应用并打包所有资源
-
-### 6. 查找构建产物
-
-构建完成后，您可以在以下位置找到安装包:
-
-- **macOS**: `dashboard/src-tauri/target/release/bundle/dmg/AstrBot_*.dmg`
-- **Windows**: `dashboard/src-tauri/target/release/bundle/msi/AstrBot_*.msi`
-- **Linux**: 
-  - `dashboard/src-tauri/target/release/bundle/deb/astrbot_*.deb`
-  - `dashboard/src-tauri/target/release/bundle/appimage/astrbot_*.AppImage`
-
-## 开发模式
-
-在开发时，您可能不想每次都完整编译 Python 后端。
-
-### 仅开发 Tauri + Vue
-```bash
-cd dashboard
-npm run tauri:dev
-```
-
-这会启动开发服务器，但不会自动启动 Python 后端。您需要手动运行:
-```bash
-uv run main.py
-```
-
-### 测试完整集成
-如果您想测试 Tauri 自动启动 Python 后端的功能:
-
-1. 先编译一次 Python 后端:
-```bash
-python build_nuitka.py
-```
-
-2. 手动复制到资源目录:
-```bash
-# macOS
-cp -r build/nuitka/main.app dashboard/src-tauri/resources/astrbot-backend.app
-
-# Windows
-copy build\nuitka\main.exe dashboard\src-tauri\resources\astrbot-backend.exe
-
-# Linux
-cp build/nuitka/main.bin dashboard/src-tauri/resources/astrbot-backend
-```
-
-3. 运行开发模式:
-```bash
-cd dashboard
-npm run tauri:dev
-```
-
-## Nuitka 构建选项说明
-
-`build_nuitka.py` 脚本使用以下关键选项:
-
-- `--standalone`: 创建包含所有依赖的独立目录
-- `--onefile`: 将所有内容打包到单个可执行文件
-- `--follow-imports`: 自动跟踪所有 Python 导入
-- `--include-package`: 明确包含特定包
-- `--include-data-dir`: 包含数据目录（插件、配置等）
-
-### 自定义构建
-
-如果您需要修改构建选项，编辑 `build_nuitka.py`:
-
-```python
-# 添加更多要包含的包
-include_packages = [
-    "astrbot",
-    "your_custom_package",
-    # ...
-]
-
-# 添加更多数据目录
-data_includes = [
-    "data/config",
-    "your_custom_data",
-    # ...
-]
-```
-
-## 常见问题
-
-### 1. Nuitka 编译失败
-**问题**: 编译时出现 "module not found" 错误
-
-**解决方案**: 在 `build_nuitka.py` 中添加缺失的包到 `include_packages` 列表
-
-### 2. 运行时找不到资源文件
-**问题**: 应用启动后提示找不到配置文件或插件
-
-**解决方案**: 确保在 `build_nuitka.py` 中使用 `--include-data-dir` 包含了所有必要的数据目录
-
-### 3. macOS 安全警告
-**问题**: macOS 提示"应用来自未知开发者"
-
-**解决方案**: 
-```bash
-# 临时解除限制
-sudo spctl --master-disable
-
-# 或者为特定应用授权
-xattr -cr /Applications/AstrBot.app
-```
-
-对于生产发布，您需要:
-1. 注册 Apple Developer 账号
-2. 对应用进行代码签名
-3. 提交公证 (Notarization)
-
-### 4. Windows Defender 报毒
-**问题**: Windows Defender 或其他杀毒软件报毒
-
-**解决方案**: 
-- 这是 Nuitka 打包程序的常见问题
-- 可以使用 `--windows-company-name` 和 `--windows-product-name` 添加元数据
-- 对于生产发布，需要购买代码签名证书
-
-### 5. Linux 依赖问题
-**问题**: 在某些 Linux 发行版上缺少共享库
-
-**解决方案**: 使用 AppImage 格式，它包含所有依赖:
-```bash
-# 构建时会自动生成 AppImage
-npm run tauri:build
-```
-
-## 优化构建大小
-
-默认的 `--onefile` 模式会生成较大的可执行文件。如果需要减小体积:
-
-1. 移除不需要的包
-2. 使用 `--standalone` 而不是 `--onefile`
-3. 排除不必要的数据文件
-
-修改 `build_nuitka.py`:
-```python
-# 移除 --onefile，使用 --standalone
-nuitka_cmd = [
-    sys.executable,
-    "-m", "nuitka",
-    "--standalone",  # 只使用 standalone
-    # "--onefile",   # 注释掉 onefile
-    # ...
-]
-```
-
-## CI/CD 集成
-
-项目已配置 GitHub Actions 工作流 (`.github/workflows/build-app.yml`)，可以自动为所有平台构建应用。
-
-推送标签时自动触发:
-```bash
-git tag v4.5.7
-git push origin v4.5.7
-```
-
-或手动触发:
-在 GitHub Actions 页面选择 "Build Desktop App" 工作流并点击 "Run workflow"
-
-## 发布清单
-
-在发布新版本前:
-
-- [ ] 更新版本号
-  - `pyproject.toml` - Python 项目版本
-  - `dashboard/package.json` - Node 项目版本
-  - `dashboard/src-tauri/Cargo.toml` - Rust 项目版本
-  - `dashboard/src-tauri/tauri.conf.json` - Tauri 配置版本
-
-- [ ] 运行代码检查
-  ```bash
-  uv run ruff check .
-  uv run ruff format .
-  ```
-
-- [ ] 本地测试构建
-  ```bash
-  python build_nuitka.py
-  cd dashboard && npm run tauri:build
-  ```
-
-- [ ] 测试安装包
-  - 安装生成的安装包
-  - 验证应用启动
-  - 验证 Python 后端自动启动
-  - 测试核心功能
-
-- [ ] 创建发布标签
-  ```bash
-  git tag -a v4.5.7 -m "Release v4.5.7"
-  git push origin v4.5.7
-  ```
-
-## 技术架构
-
-```
-┌─────────────────────────────────────┐
-│         Tauri Desktop App           │
-│  (Rust + WebView)                   │
-│                                     │
-│  ┌─────────────────────────────┐   │
-│  │   Vue.js Dashboard          │   │
-│  │   (Frontend UI)             │   │
-│  └─────────────────────────────┘   │
-│                                     │
-│  ┌─────────────────────────────┐   │
-│  │   Python Backend            │   │
-│  │   (Nuitka Compiled)         │   │
-│  │   - AstrBot Core            │   │
-│  │   - Plugins                 │   │
-│  │   - API Server              │   │
-│  └─────────────────────────────┘   │
-│                                     │
-│         HTTP/WebSocket              │
-│      localhost:6185                 │
-└─────────────────────────────────────┘
-```
-
-## 参考资源
-
-- [Nuitka 文档](https://nuitka.net/doc/user-manual.html)
-- [Tauri 文档](https://tauri.app/v1/guides/)
-- [AstrBot 文档](https://astrbot.fun)

CONTRIBUTING.md

@@ -33,20 +33,6 @@
 - 请使用英文描述您的 PR。
 - 标题请使用 `fix: `, `feat: `, `docs: `, `style: `, `refactor: `, `test: `, `chore: ` 等语义化前缀，并简要描述更改内容。如：`fix: correct login page typo`。
 
-#### 代码规范
-
-##### Core
-
-我们使用 Ruff 作为代码格式化和静态分析工具。在提交代码之前，请运行以下命令以确保代码符合规范：
-
-```bash
-ruff format .
-ruff check .
-```
-
-如果您使用 VSCode，可以安装 `Ruff` 插件。
-
-
 ## Contributing Guide
 
 First off, thanks for taking the time to contribute! ❤️
@@ -76,15 +62,4 @@ We use the `fix/` prefix for bug fixes and the `feat/` prefix for new features.
 
 #### PR Description
 - Please use English to describe your PR.
-- Use semantic prefixes like `fix: `, `feat: `, `docs: `, `style: `, `refactor: `, `test: `, `chore: ` in the title, followed by a brief description of the changes, e.g., `fix: correct login page typo`.
-
-#### Code Style
-
-##### Core
-
-We use Ruff as our code formatter and static analysis tool. Before submitting your code, please run the following commands to ensure your code adheres to the style guidelines:
-
-```bash
-ruff format .
-ruff check .
-```
+- Use semantic prefixes like `fix: `, `feat: `, `docs: `, `style: `, `refactor: `, `test: `, `chore: ` in the title, followed by a brief description of the changes, e.g., `fix: correct login page typo`.

README.md

@@ -243,10 +243,4 @@ pre-commit install
 
 </details>
 
-<div align="center">
-
 _私は、高性能ですから!_
-
-<img src="https://files.astrbot.app/watashiwa-koseino-desukara.gif" width="100"/>
-</div
-

astrbot/cli/__init__.py

@@ -1 +1 @@
-__version__ = "4.9.2"
+__version__ = "4.9.0"

astrbot/core/agent/message.py

@@ -3,7 +3,7 @@
 
 from typing import Any, ClassVar, Literal, cast
 
-from pydantic import BaseModel, GetCoreSchemaHandler, model_serializer, model_validator
+from pydantic import BaseModel, GetCoreSchemaHandler, model_validator
 from pydantic_core import core_schema
 
 
@@ -122,12 +122,10 @@ class ToolCall(BaseModel):
     extra_content: dict[str, Any] | None = None
     """Extra metadata for the tool call."""
 
-    @model_serializer(mode="wrap")
-    def serialize(self, handler):
-        data = handler(self)
+    def model_dump(self, **kwargs: Any) -> dict[str, Any]:
         if self.extra_content is None:
-            data.pop("extra_content", None)
-        return data
+            kwargs.setdefault("exclude", set()).add("extra_content")
+        return super().model_dump(**kwargs)
 
 
 class ToolCallPart(BaseModel):

astrbot/core/agent/response.py

@@ -1,8 +1,7 @@
 import typing as T
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 
 from astrbot.core.message.message_event_result import MessageChain
-from astrbot.core.provider.entities import TokenUsage
 
 
 class AgentResponseData(T.TypedDict):
@@ -13,23 +12,3 @@ class AgentResponseData(T.TypedDict):
 class AgentResponse:
     type: str
     data: AgentResponseData
-
-
-@dataclass
-class AgentStats:
-    token_usage: TokenUsage = field(default_factory=TokenUsage)
-    start_time: float = 0.0
-    end_time: float = 0.0
-    time_to_first_token: float = 0.0
-
-    @property
-    def duration(self) -> float:
-        return self.end_time - self.start_time
-
-    def to_dict(self) -> dict:
-        return {
-            "token_usage": self.token_usage.__dict__,
-            "start_time": self.start_time,
-            "end_time": self.end_time,
-            "time_to_first_token": self.time_to_first_token,
-        }

astrbot/core/agent/run_context.py

@@ -9,7 +9,7 @@ from .message import Message
 TContext = TypeVar("TContext", default=Any)
 
 
-@dataclass
+@dataclass(config={"arbitrary_types_allowed": True})
 class ContextWrapper(Generic[TContext]):
     """A context for running an agent, which can be used to pass additional data or state."""
 

astrbot/core/agent/runners/tool_loop_agent_runner.py

@@ -1,5 +1,4 @@
 import sys
-import time
 import traceback
 import typing as T
 
@@ -13,7 +12,6 @@ from mcp.types import (
 )
 
 from astrbot import logger
-from astrbot.core.message.components import Json
 from astrbot.core.message.message_event_result import (
     MessageChain,
 )
@@ -26,7 +24,7 @@ from astrbot.core.provider.provider import Provider
 
 from ..hooks import BaseAgentRunHooks
 from ..message import AssistantMessageSegment, Message, ToolCallMessageSegment
-from ..response import AgentResponseData, AgentStats
+from ..response import AgentResponseData
 from ..run_context import ContextWrapper, TContext
 from ..tool_executor import BaseFunctionToolExecutor
 from .base import AgentResponse, AgentState, BaseAgentRunner
@@ -71,9 +69,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
             )
         self.run_context.messages = messages
 
-        self.stats = AgentStats()
-        self.stats.start_time = time.time()
-
     async def _iter_llm_responses(self) -> T.AsyncGenerator[LLMResponse, None]:
         """Yields chunks *and* a final LLMResponse."""
         if self.streaming:
@@ -103,10 +98,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
 
         async for llm_response in self._iter_llm_responses():
             if llm_response.is_chunk:
-                # update ttft
-                if self.stats.time_to_first_token == 0:
-                    self.stats.time_to_first_token = time.time() - self.stats.start_time
-
                 if llm_response.result_chain:
                     yield AgentResponse(
                         type="streaming_delta",
@@ -130,10 +121,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                     )
                 continue
             llm_resp_result = llm_response
-
-            if not llm_response.is_chunk and llm_response.usage:
-                # only count the token usage of the final response for computation purpose
-                self.stats.token_usage += llm_response.usage
             break  # got final response
 
         if not llm_resp_result:
@@ -145,7 +132,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
         if llm_resp.role == "err":
             # 如果 LLM 响应错误，转换到错误状态
             self.final_llm_resp = llm_resp
-            self.stats.end_time = time.time()
             self._transition_state(AgentState.ERROR)
             yield AgentResponse(
                 type="err",
@@ -160,7 +146,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
             # 如果没有工具调用，转换到完成状态
             self.final_llm_resp = llm_resp
             self._transition_state(AgentState.DONE)
-            self.stats.end_time = time.time()
             # record the final assistant message
             self.run_context.messages.append(
                 Message(
@@ -190,19 +175,22 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
         # 如果有工具调用，还需处理工具调用
         if llm_resp.tools_call_name:
             tool_call_result_blocks = []
+            for tool_call_name in llm_resp.tools_call_name:
+                yield AgentResponse(
+                    type="tool_call",
+                    data=AgentResponseData(
+                        chain=MessageChain(type="tool_call").message(
+                            f"🔨 调用工具: {tool_call_name}"
+                        ),
+                    ),
+                )
             async for result in self._handle_function_tools(self.req, llm_resp):
                 if isinstance(result, list):
                     tool_call_result_blocks = result
                 elif isinstance(result, MessageChain):
-                    if result.type is None:
-                        # should not happen
-                        continue
-                    if result.type == "tool_direct_result":
-                        ar_type = "tool_call_result"
-                    else:
-                        ar_type = result.type
+                    result.type = "tool_call_result"
                     yield AgentResponse(
-                        type=ar_type,
+                        type="tool_call_result",
                         data=AgentResponseData(chain=result),
                     )
             # 将结果添加到上下文中
@@ -245,19 +233,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
             llm_response.tools_call_args,
             llm_response.tools_call_ids,
         ):
-            yield MessageChain(
-                type="tool_call",
-                chain=[
-                    Json(
-                        data={
-                            "id": func_tool_id,
-                            "name": func_tool_name,
-                            "args": func_tool_args,
-                            "ts": time.time(),
-                        }
-                    )
-                ],
-            )
             try:
                 if not req.func_tool:
                     return
@@ -331,6 +306,7 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                                     content=res.content[0].text,
                                 ),
                             )
+                            yield MessageChain().message(res.content[0].text)
                         elif isinstance(res.content[0], ImageContent):
                             tool_call_result_blocks.append(
                                 ToolCallMessageSegment(
@@ -352,6 +328,7 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                                         content=resource.text,
                                     ),
                                 )
+                                yield MessageChain().message(resource.text)
                             elif (
                                 isinstance(resource, BlobResourceContents)
                                 and resource.mimeType
@@ -375,22 +352,7 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                                         content="返回的数据类型不受支持",
                                     ),
                                 )
-
-                        # yield the last tool call result
-                        if tool_call_result_blocks:
-                            last_tcr_content = str(tool_call_result_blocks[-1].content)
-                            yield MessageChain(
-                                type="tool_call_result",
-                                chain=[
-                                    Json(
-                                        data={
-                                            "id": func_tool_id,
-                                            "ts": time.time(),
-                                            "result": last_tcr_content,
-                                        }
-                                    )
-                                ],
-                            )
+                                yield MessageChain().message("返回的数据类型不受支持。")
 
                     elif resp is None:
                         # Tool 直接请求发送消息给用户
@@ -400,7 +362,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                             f"{func_tool_name} 没有没有返回值或者将结果直接发送给用户，此工具调用不会被记录到历史中。"
                         )
                         self._transition_state(AgentState.DONE)
-                        self.stats.end_time = time.time()
                     else:
                         # 不应该出现其他类型
                         logger.warning(

astrbot/core/astr_agent_context.py

@@ -6,10 +6,8 @@ from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from astrbot.core.star.context import Context
 
 
-@dataclass
+@dataclass(config={"arbitrary_types_allowed": True})
 class AstrAgentContext:
-    __pydantic_config__ = {"arbitrary_types_allowed": True}
-
     context: Context
     """The star context instance"""
     event: AstrMessageEvent

astrbot/core/astr_agent_run_util.py

@@ -4,7 +4,6 @@ from collections.abc import AsyncGenerator
 from astrbot.core import logger
 from astrbot.core.agent.runners.tool_loop_agent_runner import ToolLoopAgentRunner
 from astrbot.core.astr_agent_context import AstrAgentContext
-from astrbot.core.message.components import Json
 from astrbot.core.message.message_event_result import (
     MessageChain,
     MessageEventResult,
@@ -34,27 +33,16 @@ async def run_agent(
                     msg_chain = resp.data["chain"]
                     if msg_chain.type == "tool_direct_result":
                         # tool_direct_result 用于标记 llm tool 需要直接发送给用户的内容
-                        await astr_event.send(msg_chain)
+                        await astr_event.send(resp.data["chain"])
                         continue
-                    if astr_event.get_platform_id() == "webchat":
-                        await astr_event.send(msg_chain)
                     # 对于其他情况，暂时先不处理
                     continue
                 elif resp.type == "tool_call":
                     if agent_runner.streaming:
                         # 用来标记流式响应需要分节
                         yield MessageChain(chain=[], type="break")
-
-                    if astr_event.get_platform_name() == "webchat":
+                    if show_tool_use:
                         await astr_event.send(resp.data["chain"])
-                    elif show_tool_use:
-                        json_comp = resp.data["chain"].chain[0]
-                        if isinstance(json_comp, Json):
-                            m = f"🔨 调用工具: {json_comp.data.get('name')}"
-                        else:
-                            m = "🔨 调用工具..."
-                        chain = MessageChain(type="tool_call").message(m)
-                        await astr_event.send(chain)
                     continue
 
                 if stream_to_general and resp.type == "streaming_delta":
@@ -81,15 +69,6 @@ async def run_agent(
                         continue
                     yield resp.data["chain"]  # MessageChain
             if agent_runner.done():
-                # send agent stats to webchat
-                if astr_event.get_platform_name() == "webchat":
-                    await astr_event.send(
-                        MessageChain(
-                            type="agent_stats",
-                            chain=[Json(data=agent_runner.stats.to_dict())],
-                        )
-                    )
-
                 break
 
         except Exception as e:

astrbot/core/config/default.py

@@ -4,7 +4,7 @@ import os
 
 from astrbot.core.utils.astrbot_path import get_astrbot_data_path
 
-VERSION = "4.9.2"
+VERSION = "4.9.0"
 DB_PATH = os.path.join(get_astrbot_data_path(), "data_v4.db")
 
 WEBHOOK_SUPPORTED_PLATFORMS = [
@@ -108,7 +108,6 @@ DEFAULT_CONFIG = {
         "provider_id": "",
         "dual_output": False,
         "use_file_service": False,
-        "trigger_probability": 1.0,
     },
     "provider_ltm_settings": {
         "group_icl_enable": False,
@@ -209,7 +208,7 @@ CONFIG_METADATA_2 = {
                         "callback_server_host": "0.0.0.0",
                         "port": 6196,
                     },
-                    "OneBot v11": {
+                    "QQ 个人号(OneBot v11)": {
                         "id": "default",
                         "type": "aiocqhttp",
                         "enable": False,
@@ -946,7 +945,7 @@ CONFIG_METADATA_2 = {
                         "api_base": "https://generativelanguage.googleapis.com/v1beta/openai/",
                         "timeout": 120,
                         "model_config": {
-                            "model": "gemini-3-flash-preview",
+                            "model": "gemini-1.5-flash",
                             "temperature": 0.4,
                         },
                         "custom_headers": {},
@@ -963,7 +962,7 @@ CONFIG_METADATA_2 = {
                         "api_base": "https://generativelanguage.googleapis.com/",
                         "timeout": 120,
                         "model_config": {
-                            "model": "gemini-3-flash-preview",
+                            "model": "gemini-2.0-flash-exp",
                             "temperature": 0.4,
                         },
                         "gm_resp_image_modal": False,
@@ -976,7 +975,9 @@ CONFIG_METADATA_2 = {
                             "sexually_explicit": "BLOCK_MEDIUM_AND_ABOVE",
                             "dangerous_content": "BLOCK_MEDIUM_AND_ABOVE",
                         },
-                        "gm_thinking_config": {"budget": 0, "level": "HIGH"},
+                        "gm_thinking_config": {
+                            "budget": 0,
+                        },
                         "modalities": ["text", "image", "tool_use"],
                     },
                     "DeepSeek": {
@@ -1817,24 +1818,13 @@ CONFIG_METADATA_2 = {
                         },
                     },
                     "gm_thinking_config": {
-                        "description": "Thinking Config",
+                        "description": "Gemini思考设置",
                         "type": "object",
                         "items": {
                             "budget": {
-                                "description": "Thinking Budget",
+                                "description": "思考预算",
                                 "type": "int",
-                                "hint": "Guides the model on the specific number of thinking tokens to use for reasoning. See: https://ai.google.dev/gemini-api/docs/thinking#set-budget",
-                            },
-                            "level": {
-                                "description": "Thinking Level",
-                                "type": "string",
-                                "hint": "Recommended for Gemini 3 models and onwards, lets you control reasoning behavior.See: https://ai.google.dev/gemini-api/docs/thinking#thinking-levels",
-                                "options": [
-                                    "MINIMAL",
-                                    "LOW",
-                                    "MEDIUM",
-                                    "HIGH",
-                                ],
+                                "hint": "模型应该生成的思考Token的数量，设为0关闭思考。除gemini-2.5-flash外的模型会静默忽略此参数。",
                             },
                         },
                     },
@@ -2219,9 +2209,6 @@ CONFIG_METADATA_2 = {
                     "use_file_service": {
                         "type": "bool",
                     },
-                    "trigger_probability": {
-                        "type": "float",
-                    },
                 },
             },
             "provider_ltm_settings": {
@@ -2432,14 +2419,6 @@ CONFIG_METADATA_3 = {
                             "provider_tts_settings.enable": True,
                         },
                     },
-                    "provider_tts_settings.trigger_probability": {
-                        "description": "TTS 触发概率",
-                        "type": "float",
-                        "slider": {"min": 0, "max": 1, "step": 0.05},
-                        "condition": {
-                            "provider_tts_settings.enable": True,
-                        },
-                    },
                     "provider_settings.image_caption_prompt": {
                         "description": "图片转述提示词",
                         "type": "text",
@@ -3007,7 +2986,6 @@ CONFIG_METADATA_3 = {
                         "description": "回复概率",
                         "type": "float",
                         "hint": "0.0-1.0 之间的数值",
-                        "slider": {"min": 0, "max": 1, "step": 0.05},
                         "condition": {
                             "provider_ltm_settings.active_reply.enable": True,
                         },

astrbot/core/config/i18n_utils.py

@@ -79,7 +79,6 @@ class ConfigMetadataI18n:
                             "_special",
                             "invisible",
                             "options",
-                            "slider",
                         ]:
                             if attr in field_data:
                                 field_result[attr] = field_data[attr]

astrbot/core/message/components.py

@@ -629,11 +629,12 @@ class Nodes(BaseMessageComponent):
 
 class Json(BaseMessageComponent):
     type = ComponentType.Json
-    data: dict
+    data: str | dict
+    resid: int | None = 0
 
-    def __init__(self, data: str | dict, **_):
-        if isinstance(data, str):
-            data = json.loads(data)
+    def __init__(self, data, **_):
+        if isinstance(data, dict):
+            data = json.dumps(data)
         super().__init__(data=data, **_)
 
 

astrbot/core/pipeline/respond/stage.py

@@ -119,7 +119,7 @@ class RespondStage(Stage):
 
         if (result := event.get_result()) is None:
             return False
-        if self.only_llm_result and not result.is_llm_result():
+        if self.only_llm_result and result.is_llm_result():
             return False
 
         if event.get_platform_name() in [
@@ -158,11 +158,7 @@ class RespondStage(Stage):
         result = event.get_result()
         if result is None:
             return
-        if event.get_extra("_streaming_finished", False):
-            # prevent some plugin make result content type to LLM_RESULT after streaming finished, lead to send again
-            return
         if result.result_content_type == ResultContentType.STREAMING_FINISH:
-            event.set_extra("_streaming_finished", True)
             return
 
         logger.info(

astrbot/core/pipeline/result_decorate/stage.py

@@ -1,4 +1,3 @@
-import random
 import re
 import time
 import traceback
@@ -43,18 +42,6 @@ class ResultDecorateStage(Stage):
             "forward_threshold"
         ]
 
-        trigger_probability = ctx.astrbot_config["provider_tts_settings"].get(
-            "trigger_probability",
-            1,
-        )
-        try:
-            self.tts_trigger_probability = max(
-                0.0,
-                min(float(trigger_probability), 1.0),
-            )
-        except (TypeError, ValueError):
-            self.tts_trigger_probability = 1.0
-
         # 分段回复
         self.words_count_threshold = int(
             ctx.astrbot_config["platform_settings"]["segmented_reply"][
@@ -259,14 +246,7 @@ class ResultDecorateStage(Stage):
                 and result.is_llm_result()
                 and SessionServiceManager.should_process_tts_request(event)
             ):
-                should_tts = self.tts_trigger_probability >= 1.0 or (
-                    self.tts_trigger_probability > 0.0
-                    and random.random() <= self.tts_trigger_probability
-                )
-
-                if not should_tts:
-                    logger.debug("跳过 TTS：触发概率未命中。")
-                elif not tts_provider:
+                if not tts_provider:
                     logger.warning(
                         f"会话 {event.unified_msg_origin} 未配置文本转语音模型。",
                     )

astrbot/core/platform/sources/telegram/tg_event.py

@@ -200,15 +200,6 @@ class TelegramPlatformEvent(AstrMessageEvent):
             if isinstance(chain, MessageChain):
                 if chain.type == "break":
                     # 分割符
-                    if message_id:
-                        try:
-                            await self.client.edit_message_text(
-                                text=delta,
-                                chat_id=payload["chat_id"],
-                                message_id=message_id,
-                            )
-                        except Exception as e:
-                            logger.warning(f"编辑消息失败(streaming-break): {e!s}")
                     message_id = None  # 重置消息 ID
                     delta = ""  # 重置 delta
                     continue

astrbot/core/platform/sources/webchat/webchat_event.py

@@ -1,12 +1,11 @@
 import base64
-import json
 import os
 import shutil
 import uuid
 
 from astrbot.api import logger
 from astrbot.api.event import AstrMessageEvent, MessageChain
-from astrbot.api.message_components import File, Image, Json, Plain, Record
+from astrbot.api.message_components import File, Image, Plain, Record
 from astrbot.core.utils.astrbot_path import get_astrbot_data_path
 
 from .webchat_queue_mgr import webchat_queue_mgr
@@ -42,20 +41,12 @@ class WebChatMessageEvent(AstrMessageEvent):
                 await web_chat_back_queue.put(
                     {
                         "type": "plain",
+                        "cid": cid,
                         "data": data,
                         "streaming": streaming,
                         "chain_type": message.type,
                     },
                 )
-            elif isinstance(comp, Json):
-                await web_chat_back_queue.put(
-                    {
-                        "type": "plain",
-                        "data": json.dumps(comp.data, ensure_ascii=False),
-                        "streaming": streaming,
-                        "chain_type": message.type,
-                    },
-                )
             elif isinstance(comp, Image):
                 # save image to local
                 filename = f"{str(uuid.uuid4())}.jpg"
@@ -67,6 +58,7 @@ class WebChatMessageEvent(AstrMessageEvent):
                 await web_chat_back_queue.put(
                     {
                         "type": "image",
+                        "cid": cid,
                         "data": data,
                         "streaming": streaming,
                     },
@@ -82,6 +74,7 @@ class WebChatMessageEvent(AstrMessageEvent):
                 await web_chat_back_queue.put(
                     {
                         "type": "record",
+                        "cid": cid,
                         "data": data,
                         "streaming": streaming,
                     },
@@ -98,6 +91,7 @@ class WebChatMessageEvent(AstrMessageEvent):
                 await web_chat_back_queue.put(
                     {
                         "type": "file",
+                        "cid": cid,
                         "data": data,
                         "streaming": streaming,
                     },
@@ -117,17 +111,18 @@ class WebChatMessageEvent(AstrMessageEvent):
         cid = self.session_id.split("!")[-1]
         web_chat_back_queue = webchat_queue_mgr.get_or_create_back_queue(cid)
         async for chain in generator:
-            # if chain.type == "break" and final_data:
-            #     # 分割符
-            #     await web_chat_back_queue.put(
-            #         {
-            #             "type": "break",  # break means a segment end
-            #             "data": final_data,
-            #             "streaming": True,
-            #         },
-            #     )
-            #     final_data = ""
-            #     continue
+            if chain.type == "break" and final_data:
+                # 分割符
+                await web_chat_back_queue.put(
+                    {
+                        "type": "break",  # break means a segment end
+                        "data": final_data,
+                        "streaming": True,
+                        "cid": cid,
+                    },
+                )
+                final_data = ""
+                continue
 
             r = await WebChatMessageEvent._send(
                 chain,
@@ -147,6 +142,7 @@ class WebChatMessageEvent(AstrMessageEvent):
                 "data": final_data,
                 "reasoning": reasoning_content,
                 "streaming": True,
+                "cid": cid,
             },
         )
         await super().send_streaming(generator, use_fallback)

astrbot/core/provider/entities.py

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 import base64
 import enum
 import json
@@ -201,38 +199,6 @@ class ProviderRequest:
         return ""
 
 
-@dataclass
-class TokenUsage:
-    input_other: int = 0
-    """The number of input tokens, excluding cached tokens."""
-    input_cached: int = 0
-    """The number of input cached tokens."""
-    output: int = 0
-    """The number of output tokens."""
-
-    @property
-    def total(self) -> int:
-        return self.input_other + self.input_cached + self.output
-
-    @property
-    def input(self) -> int:
-        return self.input_other + self.input_cached
-
-    def __add__(self, other: TokenUsage) -> TokenUsage:
-        return TokenUsage(
-            input_other=self.input_other + other.input_other,
-            input_cached=self.input_cached + other.input_cached,
-            output=self.output + other.output,
-        )
-
-    def __sub__(self, other: TokenUsage) -> TokenUsage:
-        return TokenUsage(
-            input_other=self.input_other - other.input_other,
-            input_cached=self.input_cached - other.input_cached,
-            output=self.output - other.output,
-        )
-
-
 @dataclass
 class LLMResponse:
     role: str
@@ -261,11 +227,6 @@ class LLMResponse:
     is_chunk: bool = False
     """Indicates if the response is a chunked response."""
 
-    id: str | None = None
-    """The ID of the response. For chunked responses, it's the ID of the chunk; for non-chunked responses, it's the ID of the response."""
-    usage: TokenUsage | None = None
-    """The usage of the response. For chunked responses, it's the usage of the chunk; for non-chunked responses, it's the usage of the response."""
-
     def __init__(
         self,
         role: str,
@@ -280,8 +241,6 @@ class LLMResponse:
         | AnthropicMessage
         | None = None,
         is_chunk: bool = False,
-        id: str | None = None,
-        usage: TokenUsage | None = None,
     ):
         """初始化 LLMResponse
 

astrbot/core/provider/sources/anthropic_source.py

@@ -6,12 +6,10 @@ from mimetypes import guess_type
 import anthropic
 from anthropic import AsyncAnthropic
 from anthropic.types import Message
-from anthropic.types.message_delta_usage import MessageDeltaUsage
-from anthropic.types.usage import Usage
 
 from astrbot import logger
 from astrbot.api.provider import Provider
-from astrbot.core.provider.entities import LLMResponse, TokenUsage
+from astrbot.core.provider.entities import LLMResponse
 from astrbot.core.provider.func_tool_manager import ToolSet
 from astrbot.core.utils.io import download_image_by_url
 
@@ -109,22 +107,6 @@ class ProviderAnthropic(Provider):
 
         return system_prompt, new_messages
 
-    def _extract_usage(self, usage: Usage) -> TokenUsage:
-        # https://docs.claude.com/en/docs/build-with-claude/prompt-caching#tracking-cache-performance
-        return TokenUsage(
-            input_other=usage.input_tokens or 0,
-            input_cached=usage.cache_read_input_tokens or 0,
-            output=usage.output_tokens,
-        )
-
-    def _update_usage(self, token_usage: TokenUsage, usage: MessageDeltaUsage) -> None:
-        if usage.input_tokens is not None:
-            token_usage.input_other = usage.input_tokens
-        if usage.cache_read_input_tokens is not None:
-            token_usage.input_cached = usage.cache_read_input_tokens
-        if usage.output_tokens is not None:
-            token_usage.output = usage.output_tokens
-
     async def _query(self, payloads: dict, tools: ToolSet | None) -> LLMResponse:
         if tools:
             if tool_list := tools.get_func_desc_anthropic_style():
@@ -149,10 +131,6 @@ class ProviderAnthropic(Provider):
                 llm_response.tools_call_args.append(content_block.input)
                 llm_response.tools_call_name.append(content_block.name)
                 llm_response.tools_call_ids.append(content_block.id)
-
-        llm_response.id = completion.id
-        llm_response.usage = self._extract_usage(completion.usage)
-
         # TODO(Soulter): 处理 end_turn 情况
         if not llm_response.completion_text and not llm_response.tools_call_args:
             raise Exception(f"Anthropic API 返回的 completion 无法解析：{completion}。")
@@ -174,16 +152,9 @@ class ProviderAnthropic(Provider):
         final_text = ""
         final_tool_calls = []
 
-        id = None
-        usage = TokenUsage()
-
         async with self.client.messages.stream(**payloads) as stream:
             assert isinstance(stream, anthropic.AsyncMessageStream)
             async for event in stream:
-                if event.type == "message_start":
-                    # the usage contains input token usage
-                    id = event.message.id
-                    usage = self._extract_usage(event.message.usage)
                 if event.type == "content_block_start":
                     if event.content_block.type == "text":
                         # 文本块开始
@@ -191,8 +162,6 @@ class ProviderAnthropic(Provider):
                             role="assistant",
                             completion_text="",
                             is_chunk=True,
-                            usage=usage,
-                            id=id,
                         )
                     elif event.content_block.type == "tool_use":
                         # 工具使用块开始，初始化缓冲区
@@ -210,8 +179,6 @@ class ProviderAnthropic(Provider):
                             role="assistant",
                             completion_text=event.delta.text,
                             is_chunk=True,
-                            usage=usage,
-                            id=id,
                         )
                     elif event.delta.type == "input_json_delta":
                         # 工具调用参数增量
@@ -248,8 +215,6 @@ class ProviderAnthropic(Provider):
                                 tools_call_name=[tool_info["name"]],
                                 tools_call_ids=[tool_info["id"]],
                                 is_chunk=True,
-                                usage=usage,
-                                id=id,
                             )
                         except json.JSONDecodeError:
                             # JSON 解析失败，跳过这个工具调用
@@ -258,17 +223,11 @@ class ProviderAnthropic(Provider):
                         # 清理缓冲区
                         del tool_use_buffer[event.index]
 
-                elif event.type == "message_delta":
-                    if event.usage:
-                        self._update_usage(usage, event.usage)
-
         # 返回最终的完整结果
         final_response = LLMResponse(
             role="assistant",
             completion_text=final_text,
             is_chunk=False,
-            usage=usage,
-            id=id,
         )
 
         if final_tool_calls:

astrbot/core/provider/sources/gemini_source.py

@@ -14,7 +14,7 @@ import astrbot.core.message.components as Comp
 from astrbot import logger
 from astrbot.api.provider import Provider
 from astrbot.core.message.message_event_result import MessageChain
-from astrbot.core.provider.entities import LLMResponse, TokenUsage
+from astrbot.core.provider.entities import LLMResponse
 from astrbot.core.provider.func_tool_manager import ToolSet
 from astrbot.core.utils.io import download_image_by_url
 
@@ -138,7 +138,7 @@ class ProviderGoogleGenAI(Provider):
             modalities = ["TEXT"]
 
         tool_list: list[types.Tool] | None = []
-        model_name = payloads.get("model", self.get_model())
+        model_name = self.get_model()
         native_coderunner = self.provider_config.get("gm_native_coderunner", False)
         native_search = self.provider_config.get("gm_native_search", False)
         url_context = self.provider_config.get("gm_url_context", False)
@@ -197,37 +197,6 @@ class ProviderGoogleGenAI(Provider):
                 types.Tool(function_declarations=func_desc["function_declarations"]),
             ]
 
-        # oper thinking config
-        thinking_config = None
-        if model_name.startswith("gemini-2.5"):
-            # The thinkingBudget parameter, introduced with the Gemini 2.5 series
-            thinking_budget = self.provider_config.get("gm_thinking_config", {}).get(
-                "budget", 0
-            )
-            if thinking_budget is not None:
-                thinking_config = types.ThinkingConfig(
-                    thinking_budget=thinking_budget,
-                )
-        elif model_name.startswith("gemini-3"):
-            # The thinkingLevel parameter, recommended for Gemini 3 models and onwards
-            # Gemini 2.5 series models don't support thinkingLevel; use thinkingBudget instead.
-            thinking_level = self.provider_config.get("gm_thinking_config", {}).get(
-                "level", "HIGH"
-            )
-            if thinking_level and isinstance(thinking_level, str):
-                thinking_level = thinking_level.upper()
-                if thinking_level not in ["MINIMAL", "LOW", "MEDIUM", "HIGH"]:
-                    logger.warning(
-                        f"Invalid thinking level: {thinking_level}, using HIGH"
-                    )
-                    thinking_level = "HIGH"
-                level = types.ThinkingLevel(thinking_level)
-                thinking_config = types.ThinkingConfig()
-                if not hasattr(types.ThinkingConfig, "thinking_level"):
-                    setattr(types.ThinkingConfig, "thinking_level", level)
-                else:
-                    thinking_config.thinking_level = level
-
         return types.GenerateContentConfig(
             system_instruction=system_instruction,
             temperature=temperature,
@@ -247,7 +216,22 @@ class ProviderGoogleGenAI(Provider):
             response_modalities=modalities,
             tools=cast(types.ToolListUnion | None, tool_list),
             safety_settings=self.safety_settings if self.safety_settings else None,
-            thinking_config=thinking_config,
+            thinking_config=(
+                types.ThinkingConfig(
+                    thinking_budget=min(
+                        int(
+                            self.provider_config.get("gm_thinking_config", {}).get(
+                                "budget",
+                                0,
+                            ),
+                        ),
+                        24576,
+                    ),
+                )
+                if "gemini-2.5-flash" in self.get_model()
+                and hasattr(types.ThinkingConfig, "thinking_budget")
+                else None
+            ),
             automatic_function_calling=types.AutomaticFunctionCallingConfig(
                 disable=True,
             ),
@@ -363,16 +347,6 @@ class ProviderGoogleGenAI(Provider):
         ]
         return "".join(thought_buf).strip()
 
-    def _extract_usage(
-        self, usage_metadata: types.GenerateContentResponseUsageMetadata
-    ) -> TokenUsage:
-        """Extract usage from candidate"""
-        return TokenUsage(
-            input_other=usage_metadata.prompt_token_count or 0,
-            input_cached=usage_metadata.cached_content_token_count or 0,
-            output=usage_metadata.candidates_token_count or 0,
-        )
-
     def _process_content_parts(
         self,
         candidate: types.Candidate,
@@ -457,8 +431,6 @@ class ProviderGoogleGenAI(Provider):
             None,
         )
 
-        model = payloads.get("model", self.get_model())
-
         modalities = ["TEXT"]
         if self.provider_config.get("gm_resp_image_modal", False):
             modalities.append("IMAGE")
@@ -477,7 +449,7 @@ class ProviderGoogleGenAI(Provider):
                     temperature,
                 )
                 result = await self.client.models.generate_content(
-                    model=model,
+                    model=self.get_model(),
                     contents=cast(types.ContentListUnion, conversation),
                     config=config,
                 )
@@ -503,11 +475,11 @@ class ProviderGoogleGenAI(Provider):
                     e.message = ""
                 if "Developer instruction is not enabled" in e.message:
                     logger.warning(
-                        f"{model} 不支持 system prompt，已自动去除(影响人格设置)",
+                        f"{self.get_model()} 不支持 system prompt，已自动去除(影响人格设置)",
                     )
                     system_instruction = None
                 elif "Function calling is not enabled" in e.message:
-                    logger.warning(f"{model} 不支持函数调用，已自动去除")
+                    logger.warning(f"{self.get_model()} 不支持函数调用，已自动去除")
                     tools = None
                 elif (
                     "Multi-modal output is not supported" in e.message
@@ -516,7 +488,7 @@ class ProviderGoogleGenAI(Provider):
                     or "only supports text output" in e.message
                 ):
                     logger.warning(
-                        f"{model} 不支持多模态输出，降级为文本模态",
+                        f"{self.get_model()} 不支持多模态输出，降级为文本模态",
                     )
                     modalities = ["TEXT"]
                 else:
@@ -529,9 +501,6 @@ class ProviderGoogleGenAI(Provider):
             result.candidates[0],
             llm_response,
         )
-        llm_response.id = result.response_id
-        if result.usage_metadata:
-            llm_response.usage = self._extract_usage(result.usage_metadata)
         return llm_response
 
     async def _query_stream(
@@ -544,7 +513,7 @@ class ProviderGoogleGenAI(Provider):
             (msg["content"] for msg in payloads["messages"] if msg["role"] == "system"),
             None,
         )
-        model = payloads.get("model", self.get_model())
+
         conversation = self._prepare_conversation(payloads)
 
         result = None
@@ -556,7 +525,7 @@ class ProviderGoogleGenAI(Provider):
                     system_instruction,
                 )
                 result = await self.client.models.generate_content_stream(
-                    model=model,
+                    model=self.get_model(),
                     contents=cast(types.ContentListUnion, conversation),
                     config=config,
                 )
@@ -566,11 +535,11 @@ class ProviderGoogleGenAI(Provider):
                     e.message = ""
                 if "Developer instruction is not enabled" in e.message:
                     logger.warning(
-                        f"{model} 不支持 system prompt，已自动去除(影响人格设置)",
+                        f"{self.get_model()} 不支持 system prompt，已自动去除(影响人格设置)",
                     )
                     system_instruction = None
                 elif "Function calling is not enabled" in e.message:
-                    logger.warning(f"{model} 不支持函数调用，已自动去除")
+                    logger.warning(f"{self.get_model()} 不支持函数调用，已自动去除")
                     tools = None
                 else:
                     raise
@@ -600,9 +569,6 @@ class ProviderGoogleGenAI(Provider):
                     chunk.candidates[0],
                     llm_response,
                 )
-                llm_response.id = chunk.response_id
-                if chunk.usage_metadata:
-                    llm_response.usage = self._extract_usage(chunk.usage_metadata)
                 yield llm_response
                 return
 
@@ -630,9 +596,6 @@ class ProviderGoogleGenAI(Provider):
                         chunk.candidates[0],
                         final_response,
                     )
-                    final_response.id = chunk.response_id
-                    if chunk.usage_metadata:
-                        final_response.usage = self._extract_usage(chunk.usage_metadata)
                 break
 
         # Yield final complete response with accumulated text

astrbot/core/provider/sources/openai_source.py

@@ -12,7 +12,6 @@ from openai._exceptions import NotFoundError
 from openai.lib.streaming.chat._completions import ChatCompletionStreamState
 from openai.types.chat.chat_completion import ChatCompletion
 from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
-from openai.types.completion_usage import CompletionUsage
 
 import astrbot.core.message.components as Comp
 from astrbot import logger
@@ -20,7 +19,7 @@ from astrbot.api.provider import Provider
 from astrbot.core.agent.message import Message
 from astrbot.core.agent.tool import ToolSet
 from astrbot.core.message.message_event_result import MessageChain
-from astrbot.core.provider.entities import LLMResponse, TokenUsage, ToolCallsResult
+from astrbot.core.provider.entities import LLMResponse, ToolCallsResult
 from astrbot.core.utils.io import download_image_by_url
 
 from ..register import register_provider_adapter
@@ -209,7 +208,6 @@ class ProviderOpenAIOfficial(Provider):
             # handle the content delta
             reasoning = self._extract_reasoning_content(chunk)
             _y = False
-            llm_response.id = chunk.id
             if reasoning:
                 llm_response.reasoning_content = reasoning
                 _y = True
@@ -219,8 +217,6 @@ class ProviderOpenAIOfficial(Provider):
                     chain=[Comp.Plain(completion_text)],
                 )
                 _y = True
-            if chunk.usage:
-                llm_response.usage = self._extract_usage(chunk.usage)
             if _y:
                 yield llm_response
 
@@ -249,15 +245,6 @@ class ProviderOpenAIOfficial(Provider):
                 reasoning_text = str(reasoning_attr)
         return reasoning_text
 
-    def _extract_usage(self, usage: CompletionUsage) -> TokenUsage:
-        ptd = usage.prompt_tokens_details
-        cached = ptd.cached_tokens if ptd and ptd.cached_tokens else 0
-        return TokenUsage(
-            input_other=usage.prompt_tokens - cached,
-            input_cached=ptd.cached_tokens if ptd and ptd.cached_tokens else 0,
-            output=usage.completion_tokens,
-        )
-
     async def _parse_openai_completion(
         self, completion: ChatCompletion, tools: ToolSet | None
     ) -> LLMResponse:
@@ -334,10 +321,6 @@ class ProviderOpenAIOfficial(Provider):
             raise Exception(f"API 返回的 completion 无法解析：{completion}。")
 
         llm_response.raw_completion = completion
-        llm_response.id = completion.id
-
-        if completion.usage:
-            llm_response.usage = self._extract_usage(completion.usage)
 
         return llm_response
 

astrbot/core/star/__init__.py

@@ -2,19 +2,15 @@ from astrbot.core import html_renderer
 from astrbot.core.provider import Provider
 from astrbot.core.star.star_tools import StarTools
 from astrbot.core.utils.command_parser import CommandParserMixin
-from astrbot.core.utils.plugin_kv_store import PluginKVStoreMixin
 
 from .context import Context
 from .star import StarMetadata, star_map, star_registry
 from .star_manager import PluginManager
 
 
-class Star(CommandParserMixin, PluginKVStoreMixin):
+class Star(CommandParserMixin):
     """所有插件（Star）的父类，所有插件都应该继承于这个类"""
 
-    author: str
-    name: str
-
     def __init__(self, context: Context, config: dict | None = None):
         StarTools.initialize(context)
         self.context = context

astrbot/core/star/context.py

@@ -296,10 +296,6 @@ class Context:
             provider_type=ProviderType.CHAT_COMPLETION,
             umo=umo,
         )
-        if prov is None:
-            raise ProviderNotFoundError(
-                "provider not found, please choose provider first"
-            )
         if not isinstance(prov, Provider):
             raise ValueError("返回的 Provider 不是 Provider 类型")
         return prov

astrbot/core/star/star_manager.py

@@ -468,18 +468,6 @@ class PluginManager:
                             metadata.star_cls = metadata.star_cls_type(
                                 context=self.context,
                             )
-
-                        p_name = (metadata.name or "unknown").lower().replace("/", "_")
-                        p_author = (
-                            (metadata.author or "unknown").lower().replace("/", "_")
-                        )
-                        setattr(metadata.star_cls, "name", p_name)
-                        setattr(metadata.star_cls, "author", p_author)
-                        setattr(
-                            metadata.star_cls,
-                            "plugin_id",
-                            f"{p_author}/{p_name}",
-                        )
                     else:
                         logger.info(f"插件 {metadata.name} 已被禁用。")
 

astrbot/core/utils/plugin_kv_store.py

@@ -1,28 +0,0 @@
-from typing import TypeVar
-
-from astrbot.core import sp
-
-SUPPORTED_VALUE_TYPES = int | float | str | bytes | bool | dict | list | None
-_VT = TypeVar("_VT")
-
-
-class PluginKVStoreMixin:
-    """为插件提供键值存储功能的 Mixin 类"""
-
-    plugin_id: str
-
-    async def put_kv_data(
-        self,
-        key: str,
-        value: SUPPORTED_VALUE_TYPES,
-    ) -> None:
-        """为指定插件存储一个键值对"""
-        await sp.put_async("plugin", self.plugin_id, key, value)
-
-    async def get_kv_data(self, key: str, default: _VT) -> _VT | None:
-        """获取指定插件存储的键值对"""
-        return await sp.get_async("plugin", self.plugin_id, key, default)
-
-    async def delete_kv_data(self, key: str) -> None:
-        """删除指定插件存储的键值对"""
-        await sp.remove_async("plugin", self.plugin_id, key)

astrbot/dashboard/routes/chat.py

@@ -227,19 +227,16 @@ class ChatRoute(Route):
         text: str,
         media_parts: list,
         reasoning: str,
-        agent_stats: dict,
     ):
         """保存 bot 消息到历史记录，返回保存的记录"""
         bot_message_parts = []
-        bot_message_parts.extend(media_parts)
         if text:
             bot_message_parts.append({"type": "plain", "text": text})
+        bot_message_parts.extend(media_parts)
 
         new_his = {"type": "bot", "message": bot_message_parts}
         if reasoning:
             new_his["reasoning"] = reasoning
-        if agent_stats:
-            new_his["agent_stats"] = agent_stats
 
         record = await self.platform_history_mgr.insert(
             platform_id="webchat",
@@ -297,8 +294,7 @@ class ChatRoute(Route):
             accumulated_parts = []
             accumulated_text = ""
             accumulated_reasoning = ""
-            tool_calls = {}
-            agent_stats = {}
+
             try:
                 async with track_conversation(self.running_convs, webchat_conv_id):
                     while True:
@@ -318,16 +314,6 @@ class ChatRoute(Route):
                         result_text = result["data"]
                         msg_type = result.get("type")
                         streaming = result.get("streaming", False)
-                        chain_type = result.get("chain_type")
-
-                        if chain_type == "agent_stats":
-                            stats_info = {
-                                "type": "agent_stats",
-                                "data": json.loads(result_text),
-                            }
-                            yield f"data: {json.dumps(stats_info, ensure_ascii=False)}\n\n"
-                            agent_stats = stats_info["data"]
-                            continue
 
                         # 发送 SSE 数据
                         try:
@@ -349,35 +335,11 @@ class ChatRoute(Route):
 
                         # 累积消息部分
                         if msg_type == "plain":
-                            chain_type = result.get("chain_type")
-                            if chain_type == "tool_call":
-                                tool_call = json.loads(result_text)
-                                tool_calls[tool_call.get("id")] = tool_call
-                                if accumulated_text:
-                                    # 如果累积了文本，则先保存文本
-                                    accumulated_parts.append(
-                                        {"type": "plain", "text": accumulated_text}
-                                    )
-                                    accumulated_text = ""
-                            elif chain_type == "tool_call_result":
-                                tcr = json.loads(result_text)
-                                tc_id = tcr.get("id")
-                                if tc_id in tool_calls:
-                                    tool_calls[tc_id]["result"] = tcr.get("result")
-                                    tool_calls[tc_id]["finished_ts"] = tcr.get("ts")
-                                accumulated_parts.append(
-                                    {
-                                        "type": "tool_call",
-                                        "tool_calls": [tool_calls[tc_id]],
-                                    }
-                                )
-                                tool_calls.pop(tc_id, None)
-                            elif chain_type == "reasoning":
+                            chain_type = result.get("chain_type", "normal")
+                            if chain_type == "reasoning":
                                 accumulated_reasoning += result_text
-                            elif streaming:
-                                accumulated_text += result_text
                             else:
-                                accumulated_text = result_text
+                                accumulated_text += result_text
                         elif msg_type == "image":
                             filename = result_text.replace("[IMAGE]", "")
                             part = await self._create_attachment_from_file(
@@ -405,20 +367,15 @@ class ChatRoute(Route):
                         if msg_type == "end":
                             break
                         elif (
-                            (streaming and msg_type == "complete") or not streaming
-                            # or msg_type == "break"
+                            (streaming and msg_type == "complete")
+                            or not streaming
+                            or msg_type == "break"
                         ):
-                            if (
-                                chain_type == "tool_call"
-                                or chain_type == "tool_call_result"
-                            ):
-                                continue
                             saved_record = await self._save_bot_message(
                                 webchat_conv_id,
                                 accumulated_text,
                                 accumulated_parts,
                                 accumulated_reasoning,
-                                agent_stats,
                             )
                             # 发送保存的消息信息给前端
                             if saved_record and not client_disconnected:
@@ -433,11 +390,11 @@ class ChatRoute(Route):
                                     yield f"data: {json.dumps(saved_info, ensure_ascii=False)}\n\n"
                                 except Exception:
                                     pass
-                            accumulated_parts = []
-                            accumulated_text = ""
-                            accumulated_reasoning = ""
-                            tool_calls = {}
-                            agent_stats = {}
+                            # 重置累积变量 (对于 break 后的下一段消息)
+                            if msg_type == "break":
+                                accumulated_parts = []
+                                accumulated_text = ""
+                                accumulated_reasoning = ""
             except BaseException as e:
                 logger.exception(f"WebChat stream unexpected error: {e}", exc_info=True)
 

build_nuitka.py

@@ -1,134 +0,0 @@
-#!/usr/bin/env python3
-"""
-Use Nuitka to build the AstrBot project into standalone executables
-"""
-
-import os
-import platform
-import subprocess
-import sys
-from pathlib import Path
-
-
-def get_platform_info():
-    """fetch the current platform information"""
-    system = platform.system()
-    machine = platform.machine()
-    return system, machine
-
-
-def build_with_nuitka():
-    """use Nuitka to build the project"""
-    system, machine = get_platform_info()
-
-    print(f"🚀 Starting build for {system} ({machine}) platform...")
-
-    # Output directory
-    output_dir = Path("build/nuitka")
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Base Nuitka command
-    nuitka_cmd = [
-        sys.executable,
-        "-m",
-        "nuitka",
-        "--standalone",  # Create standalone directory
-        "--onefile",  # Single file mode
-        "--follow-imports",  # Follow all imports
-        "--enable-plugin=multiprocessing",  # Enable multiprocessing support
-        "--output-dir=build/nuitka",  # Output directory
-        "--quiet",  # Reduce output verbosity
-        "--assume-yes-for-downloads",  # Automatically download dependencies
-        "--jobs=4",  # Use multiple CPU cores
-    ]
-
-    # include specific packages
-    include_packages = [
-        "astrbot",
-    ]
-
-    for pkg in include_packages:
-        nuitka_cmd.extend([f"--include-package={pkg}"])
-
-    # include data directories
-    # data_includes = [
-    #     "data/config",
-    #     "data/plugins",
-    #     "data/temp",
-    # ]
-
-    # for data_dir in data_includes:
-    #     if os.path.exists(data_dir):
-    #         nuitka_cmd.extend([f"--include-data-dir={data_dir}={data_dir}"])
-
-    # include packages directory (built-in plugins)
-    # if os.path.exists("packages"):
-    #     nuitka_cmd.extend(["--include-data-dir=packages=packages"])
-
-    # Platform specific settings
-    if system == "Darwin":  # macOS
-        nuitka_cmd.extend(
-            [
-                "--macos-create-app-bundle",  # Create .app bundle
-                "--macos-app-name=AstrBot",
-            ]
-        )
-        # macOS icon (if exists)
-        icon_path = "dashboard/src-tauri/icons/icon.icns"
-        if os.path.exists(icon_path):
-            nuitka_cmd.extend([f"--macos-app-icon={icon_path}"])
-    elif system == "Windows":
-        nuitka_cmd.extend(
-            [
-                "--windows-console-mode=disable",  # 无控制台窗口
-            ]
-        )
-        # Windows icon (if exists)
-        icon_path = "dashboard/src-tauri/icons/icon.ico"
-        if os.path.exists(icon_path):
-            nuitka_cmd.extend([f"--windows-icon-from-ico={icon_path}"])
-
-    # Main file to compile
-    nuitka_cmd.append("main.py")
-
-    print(f"📦 Executing command: {' '.join(nuitka_cmd)}")
-
-    try:
-        subprocess.run(nuitka_cmd, check=True)
-        print("✅ Nuitka build successful!")
-
-        # Find the generated executable
-        if system == "Darwin":
-            built_file = list(output_dir.glob("*.app"))
-            if built_file:
-                print(f"Generated macOS app: {built_file[0]}")
-        elif system == "Windows":
-            built_file = list(output_dir.glob("*.exe"))
-            if built_file:
-                print(f"Generated Windows executable: {built_file[0]}")
-        else:  # Linux
-            built_file = list(output_dir.glob("main.bin"))
-            if built_file:
-                print(f"Generated Linux executable: {built_file[0]}")
-
-        return True
-    except subprocess.CalledProcessError as e:
-        print(f"❌ Nuitka build failed: {e}")
-        return False
-
-
-if __name__ == "__main__":
-    print("=" * 60)
-    print("AstrBot Nuitka Builder")
-    print("=" * 60)
-
-    # 构建
-    if build_with_nuitka():
-        print("\n" + "=" * 60)
-        print("🎉 Build Complete!")
-        print("=" * 60)
-    else:
-        print("\n" + "=" * 60)
-        print("❌ Build Failed")
-        print("=" * 60)
-        sys.exit(1)

build_pyinstaller.py

@@ -1,134 +0,0 @@
-#!/usr/bin/env python3
-"""
-Use PyInstaller to build the AstrBot project into standalone executables
-"""
-
-import platform
-import subprocess
-import sys
-from pathlib import Path
-
-
-def get_platform_info():
-    """fetch the current platform information"""
-    system = platform.system()
-    machine = platform.machine()
-    return system, machine
-
-
-def build_with_pyinstaller():
-    """use PyInstaller to build the project"""
-    system, machine = get_platform_info()
-
-    print(f"🚀 Starting build for {system} ({machine}) platform...")
-
-    # Output directory
-    output_dir = Path("build/pyinstaller")
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Base PyInstaller command
-    pyinstaller_cmd = [
-        sys.executable,
-        "-m",
-        "PyInstaller",
-        "--clean",  # Clean cache before build
-        "--noconfirm",  # Replace output directory without asking
-        "--onefile",  # Single file mode
-        "--distpath=build/pyinstaller/dist",  # Distribution directory
-        "--workpath=build/pyinstaller/build",  # Work directory
-        "--specpath=build/pyinstaller",  # Spec file directory
-        "--name=AstrBot",  # Output executable name
-    ]
-    # Platform specific settings
-    # if system == "Darwin":  # macOS
-    #     # macOS icon (if exists)
-    #     icon_path = "dashboard/src-tauri/icons/icon.icns"
-    #     if os.path.exists(icon_path):
-    #         pyinstaller_cmd.extend([f"--icon={icon_path}"])
-    #     # Create .app bundle
-    #     pyinstaller_cmd.extend(["--windowed"])
-    # elif system == "Windows":
-    #     # Windows icon (if exists)
-    #     icon_path = "dashboard/src-tauri/icons/icon.ico"
-    #     if os.path.exists(icon_path):
-    #         pyinstaller_cmd.extend([f"--icon={icon_path}"])
-    #     # No console window
-    #     pyinstaller_cmd.extend(["--windowed"])
-    # else:  # Linux
-    #     pyinstaller_cmd.extend(["--console"])
-
-    # Main file to compile
-    pyinstaller_cmd.append("main.py")
-
-    print(f"📦 Executing command: {' '.join(pyinstaller_cmd)}")
-
-    try:
-        subprocess.run(pyinstaller_cmd, check=True)
-        print("✅ PyInstaller build successful!")
-
-        # Find the generated executable
-        dist_dir = output_dir / "dist"
-        if system == "Darwin":
-            built_file = list(dist_dir.glob("AstrBot.app"))
-            if not built_file:
-                built_file = list(dist_dir.glob("AstrBot"))
-            if built_file:
-                print(f"📱 Generated macOS app: {built_file[0]}")
-        elif system == "Windows":
-            built_file = list(dist_dir.glob("AstrBot.exe"))
-            if built_file:
-                print(f"💻 Generated Windows executable: {built_file[0]}")
-        else:  # Linux
-            built_file = list(dist_dir.glob("AstrBot"))
-            if built_file:
-                print(f"🐧 Generated Linux executable: {built_file[0]}")
-
-        print(f"\n📁 Output directory: {dist_dir.absolute()}")
-        return True
-    except subprocess.CalledProcessError as e:
-        print(f"❌ PyInstaller build failed: {e}")
-        return False
-    except Exception as e:
-        print(f"❌ Unexpected error: {e}")
-        return False
-
-
-def install_pyinstaller():
-    """Install PyInstaller if not already installed"""
-    try:
-        import PyInstaller
-
-        print(f"✅ PyInstaller already installed (version {PyInstaller.__version__})")
-        return True
-    except ImportError:
-        print("📥 PyInstaller not found, installing...")
-        try:
-            subprocess.run(
-                [sys.executable, "-m", "pip", "install", "pyinstaller"], check=True
-            )
-            print("✅ PyInstaller installed successfully!")
-            return True
-        except subprocess.CalledProcessError as e:
-            print(f"❌ Failed to install PyInstaller: {e}")
-            return False
-
-
-if __name__ == "__main__":
-    print("=" * 60)
-    print("AstrBot PyInstaller Builder")
-    print("=" * 60)
-
-    # Check and install PyInstaller
-    if not install_pyinstaller():
-        sys.exit(1)
-
-    # Build
-    if build_with_pyinstaller():
-        print("\n" + "=" * 60)
-        print("🎉 Build Complete!")
-        print("=" * 60)
-    else:
-        print("\n" + "=" * 60)
-        print("❌ Build Failed")
-        print("=" * 60)
-        sys.exit(1)

changelogs/v4.9.1.md

@@ -1,3 +0,0 @@
-## What's Changed
-
--

changelogs/v4.9.2.md

@@ -1,17 +0,0 @@
-## What's Changed
-
-### 修复
-
-- 企业自部署飞书（自定义 domain）可以接收消息但无法发送消息的问题。
-- 安装插件 Dialog 的深色样式问题。
-
-### 优化
-
-- 避免某些插件在流式响应结束后重d复发送消息的问题。
-
-### 新增
-
-- 支持在对话管理批量导出对话轨迹数据为 `jsonl` 格式文件。入口：WebUI -> 对话管理 -> 批量选中 -> 导出。
-- 支持对 TTS（文本转语音）设置概率触发。
-- （插件开发）支持在 schema 中对 float 和 int 类型设置 `slider` 滑块控件。例如 `slider: {min: 0, max: 1, step: 0.1}`。
-- （插件开发）支持 key-value 存储功能。例如使用 `await self.put_kv_data("key", value)`, `await self.get_kv_data("key", default_value)` 和 `await self.delete_kv_data("key")`。

dashboard/TAURI_README.md

@@ -1,225 +0,0 @@
-# AstrBot Dashboard - Tauri 桌面应用
-
-本项目现已支持通过 Tauri 构建为桌面应用，同时保持与 Web 版本的兼容性。
-
-## 环境要求
-
-### 系统依赖
-
-**macOS:**
-```bash
-# 安装 Xcode Command Line Tools
-xcode-select --install
-```
-
-**Windows:**
-- 安装 [Microsoft Visual Studio C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
-- 安装 [WebView2](https://developer.microsoft.com/en-us/microsoft-edge/webview2/)
-
-**Linux (Ubuntu/Debian):**
-```bash
-sudo apt update
-sudo apt install libwebkit2gtk-4.0-dev \
-    build-essential \
-    curl \
-    wget \
-    file \
-    libssl-dev \
-    libgtk-3-dev \
-    libayatana-appindicator3-dev \
-    librsvg2-dev
-```
-
-### Rust 环境
-
-```bash
-# 安装 Rust
-curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
-
-# 验证安装
-rustc --version
-cargo --version
-```
-
-## 安装依赖
-
-```bash
-cd dashboard
-npm install
-```
-
-## 开发模式
-
-### Web 端开发（不变）
-
-```bash
-npm run dev
-```
-
-访问 http://localhost:3000
-
-### 桌面端开发
-
-```bash
-npm run tauri:dev
-```
-
-这会同时启动：
-1. Vite 开发服务器（端口 3000）
-2. Tauri 桌面应用窗口
-
-热重载功能正常工作，修改代码后会自动刷新。
-
-## 构建
-
-### Web 端构建（不变）
-
-```bash
-npm run build
-```
-
-输出目录：`dist/`
-
-### 桌面端构建
-
-```bash
-npm run tauri:build
-```
-
-构建产物位置：
-- **macOS**: `src-tauri/target/release/bundle/dmg/`
-- **Windows**: `src-tauri/target/release/bundle/msi/`
-- **Linux**: `src-tauri/target/release/bundle/deb/` 或 `appimage/`
-
-## 图标设置
-
-### 自动生成图标
-
-准备一个至少 512x512 像素的 PNG 图标，然后运行：
-
-```bash
-npm run tauri icon path/to/your/icon.png
-```
-
-### 手动设置图标
-
-将以下图标放入 `src-tauri/icons/` 目录：
-- `32x32.png`
-- `128x128.png`
-- `128x128@2x.png`
-- `icon.icns` (macOS)
-- `icon.ico` (Windows)
-
-## 代码兼容性
-
-项目已配置为同时支持 Web 和桌面端，使用相同的代码库。
-
-### 环境检测工具
-
-在 `src/utils/tauri.ts` 中提供了环境检测工具：
-
-```typescript
-import { isTauri, isWeb, PlatformAPI } from '@/utils/tauri';
-
-// 检测运行环境
-if (isTauri()) {
-  console.log('运行在桌面应用中');
-} else {
-  console.log('运行在浏览器中');
-}
-
-// 获取正确的 API 端点
-const baseURL = PlatformAPI.getBaseURL();
-```
-
-### API 调用注意事项
-
-- **Web 端**: 使用 Vite 代理，API 路径为 `/api/*`
-- **桌面端**: 直接连接到 `http://127.0.0.1:6185`
-
-已在 `PlatformAPI.getBaseURL()` 中处理，使用 axios 时：
-
-```typescript
-import axios from 'axios';
-import { PlatformAPI } from '@/utils/tauri';
-
-const api = axios.create({
-  baseURL: PlatformAPI.getBaseURL()
-});
-```
-
-## 配置说明
-
-### tauri.conf.json
-
-主要配置项：
-- `build.devPath`: 开发服务器地址（http://localhost:3000）
-- `build.distDir`: 构建输出目录（../dist）
-- `tauri.allowlist`: API 权限配置
-- `tauri.windows`: 窗口配置（大小、标题等）
-
-### 安全性
-
-默认配置已启用必要的权限：
-- 文件系统访问（限定在 APPDATA 目录）
-- HTTP 请求（限定到本地后端）
-- 窗口控制
-- 对话框（打开/保存文件）
-
-可在 `tauri.conf.json` 的 `allowlist` 部分调整权限。
-
-## 后端连接
-
-桌面应用需要后端服务运行在 `http://127.0.0.1:6185`。
-
-### 启动流程
-
-1. 启动 AstrBot 后端:
-   ```bash
-   cd /path/to/AstrBot
-   uv run main.py
-   ```
-
-2. 启动桌面应用:
-   ```bash
-   cd dashboard
-   npm run tauri:dev
-   ```
-
-或直接运行打包后的应用（后端需要已启动）。
-
-## 常见问题
-
-### Q: 桌面应用无法连接到后端？
-
-确保：
-1. AstrBot 后端正在运行（`uv run main.py`）
-2. 后端监听在 `127.0.0.1:6185`
-3. 防火墙未阻止连接
-
-### Q: 图标未显示？
-
-检查 `src-tauri/icons/` 目录中是否有所需的图标文件，或使用 `npm run tauri icon` 命令生成。
-
-### Q: 构建失败？
-
-- 确保已安装 Rust 和系统依赖
-- 运行 `cargo clean` 清理缓存后重试
-- 检查 Rust 版本（需要 1.60+）
-
-### Q: Web 端功能是否受影响？
-
-不受影响。`npm run dev` 和 `npm run build` 的行为完全不变。
-
-## 开发建议
-
-1. **优先使用 Web 端开发**: 更快的热重载，更好的调试体验
-2. **定期测试桌面端**: 确保跨平台兼容性
-3. **使用环境检测**: 针对不同平台提供最佳体验
-4. **注意 API 差异**: Web 和桌面端的某些 API 可能有差异
-
-## 更多资源
-
-- [Tauri 官方文档](https://tauri.app/)
-- [Tauri API 参考](https://tauri.app/v1/api/js/)
-- [Tauri Discord 社区](https://discord.com/invite/tauri)

dashboard/package.json

@@ -10,14 +10,10 @@
     "build-prod": "vue-tsc --noEmit && vite build --base=/vue/free/",
     "preview": "vite preview --port 5050",
     "typecheck": "vue-tsc --noEmit",
-    "lint": "eslint . --ext .vue,.js,.jsx,.cjs,.mjs,.ts,.tsx,.cts,.mts --fix --ignore-path .gitignore",
-    "tauri": "tauri",
-    "tauri:dev": "tauri dev",
-    "tauri:build": "tauri build"
+    "lint": "eslint . --ext .vue,.js,.jsx,.cjs,.mjs,.ts,.tsx,.cts,.mts --fix --ignore-path .gitignore"
   },
   "dependencies": {
     "@guolao/vue-monaco-editor": "^1.5.4",
-    "@tauri-apps/api": "^2.9.0",
     "@tiptap/starter-kit": "2.1.7",
     "@tiptap/vue-3": "2.1.7",
     "apexcharts": "3.42.0",
@@ -47,7 +43,6 @@
   "devDependencies": {
     "@mdi/font": "7.2.96",
     "@rushstack/eslint-patch": "1.3.3",
-    "@tauri-apps/cli": "^2.9.4",
     "@types/chance": "1.1.3",
     "@types/markdown-it": "^14.1.2",
     "@types/node": "^20.5.7",

dashboard/src-tauri/.gitignore

@@ -1,3 +0,0 @@
-# Tauri specific
-src-tauri/target/
-src-tauri/WixTools/

dashboard/src-tauri/Cargo.toml

@@ -1,27 +0,0 @@
-[package]
-name = "astrbot-dashboard"
-version = "4.5.6"
-description = "AstrBot"
-authors = ["AstrBot Team"]
-license = "AGPL-3.0"
-repository = "https://github.com/AstrBotDevs/AstrBot"
-default-run = "astrbot-dashboard"
-edition = "2021"
-rust-version = "1.91.0"
-
-# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
-
-[build-dependencies]
-tauri-build = { version = "2", features = [] }
-
-[dependencies]
-serde_json = "1.0"
-serde = { version = "1.0", features = ["derive"] }
-tauri = { version = "2.9.2", features = ["macos-private-api", "protocol-asset"] }
-tauri-plugin-opener = "2"
-
-[features]
-# this feature is used for production builds or when `devPath` points to the filesystem and the built-in dev server is disabled.
-# If you use cargo directly instead of tauri's cli you can use this feature flag to switch between tauri's `dev` and `build` modes.
-# DO NOT REMOVE!!
-custom-protocol = [ "tauri/custom-protocol" ]

dashboard/src-tauri/build.rs

@@ -1,3 +0,0 @@
-fn main() {
-    tauri_build::build()
-}

dashboard/src-tauri/gen/schemas/capabilities.json

@@ -1 +0,0 @@
-{}

dashboard/src-tauri/icons/android/mipmap-anydpi-v26/ic_launcher.xml

@@ -1,5 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
-  <foreground android:drawable="@mipmap/ic_launcher_foreground"/>
-  <background android:drawable="@color/ic_launcher_background"/>
-</adaptive-icon>

dashboard/src-tauri/icons/android/values/ic_launcher_background.xml

@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<resources>
-  <color name="ic_launcher_background">#fff</color>
-</resources>

dashboard/src-tauri/src/main.rs

@@ -1,104 +0,0 @@
-// Prevents additional console window on Windows in release, DO NOT REMOVE!!
-#![cfg_attr(not(debug_assertions), windows_subsystem = "windows")]
-
-use std::process::{Child, Command};
-use std::sync::Mutex;
-use tauri::{AppHandle, Emitter, Listener, Manager, State};
-
-struct BackendProcess(Mutex<Option<Child>>);
-
-fn start_backend_process(app_handle: &AppHandle) -> Option<Child> {
-    #[cfg(target_os = "macos")]
-    let backend_path = "astrbot-backend.app/Contents/MacOS/main";
-
-    #[cfg(target_os = "windows")]
-    let backend_path = "astrbot-backend.exe";
-
-    #[cfg(target_os = "linux")]
-    let backend_path = "astrbot-backend";
-
-    // 获取资源目录
-    let resource_dir = match app_handle
-        .path()
-        .resource_dir()
-    {
-        Ok(dir) => dir,
-        Err(e) => {
-            eprintln!("Failed to get resource directory: {}", e);
-            return None;
-        }
-    };
-
-    let full_backend_path = resource_dir.join(backend_path);
-
-    println!("Starting backend process at: {:?}", full_backend_path);
-
-    match Command::new(&full_backend_path).spawn() {
-        Ok(child) => {
-            println!(
-                "Backend process started successfully with PID: {}",
-                child.id()
-            );
-            Some(child)
-        }
-        Err(e) => {
-            eprintln!("Failed to start backend process: {}", e);
-            None
-        }
-    }
-}
-
-#[tauri::command]
-fn restart_backend(
-    app_handle: AppHandle,
-    backend_state: State<BackendProcess>,
-) -> Result<String, String> {
-    let mut backend = backend_state.0.lock().unwrap();
-
-    // 停止现有进程
-    if let Some(mut child) = backend.take() {
-        let _ = child.kill();
-        let _ = child.wait();
-    }
-
-    // 启动新进程
-    *backend = start_backend_process(&app_handle);
-
-    if backend.is_some() {
-        Ok("Backend restarted successfully".to_string())
-    } else {
-        Err("Failed to restart backend".to_string())
-    }
-}
-
-#[cfg_attr(mobile, tauri::mobile_entry_point)]
-pub fn run() {
-    tauri::Builder::default()
-        .setup(|app| {
-            // 启动后端进程
-            let backend_process = start_backend_process(app.handle());
-            app.manage(BackendProcess(Mutex::new(backend_process)));
-            Ok(())
-        })
-        .plugin(tauri_plugin_opener::init())
-        .invoke_handler(tauri::generate_handler![restart_backend])
-        .on_window_event(|window, event| {
-            if let tauri::WindowEvent::CloseRequested { .. } = event {
-                // 关闭窗口时清理后端进程
-                if let Some(backend_state) = window.app_handle().try_state::<BackendProcess>() {
-                    let mut backend = backend_state.0.lock().unwrap();
-                    if let Some(mut child) = backend.take() {
-                        let _ = child.kill();
-                        let _ = child.wait();
-                    }
-                }
-            }
-        })
-        .run(tauri::generate_context!())
-        .expect("error while running tauri application");
-}
-
-fn main() {
-    run();
-}
-

dashboard/src-tauri/tauri.conf.json

@@ -1,53 +0,0 @@
-{
-  "$schema": "https://schema.tauri.app/config/2",
-  "productName": "AstrBot",
-  "version": "4.5.6",
-  "identifier": "com.astrbot.app",
-  "build": {
-    "beforeDevCommand": "pnpm dev",
-    "devUrl": "http://localhost:3000",
-    "beforeBuildCommand": "pnpm build",
-    "frontendDist": "../dist"
-  },
-  "app": {
-    "withGlobalTauri": true,
-    "macOSPrivateApi": true,
-    "windows": [
-      {
-        "title": "AstrBot",
-        "label": "main",
-        "url": "/",
-        "width": 1400,
-        "height": 900
-      }
-    ],
-    "security": {
-      "csp": null,
-      "assetProtocol": {
-        "enable": true,
-        "scope": [
-          "$APPDATA/**"
-        ]
-      }
-    }
-  },
-  "bundle": {
-    "active": true,
-    "targets": "all",
-    "icon": [
-      "icons/32x32.png",
-      "icons/128x128.png",
-      "icons/128x128@2x.png",
-      "icons/icon.icns",
-      "icons/icon.ico"
-    ],
-    "resources": [
-      "resources/*"
-    ]
-  },
-  "plugins": {
-    "fs": {
-      "requireLiteralLeadingDot": false
-    }
-  }
-}

dashboard/src/components/chat/Chat.vue

@@ -575,9 +575,5 @@ onBeforeUnmount(() => {
     .chat-page-container {
         padding: 0 !important;
     }
-
-    .conversation-header {
-        padding: 2px;
-    }
 }
 </style>